都市公園の多機能性を形成する景観コンテキスト、管理強度、植生不均一性の役割 – 成都を事例として

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Researchers studying urban ecosystem services can benefit from the stratified sampling and spatial regression methodology, particularly the GWR model uncovering local drivers.

🏢実務担当者:Urban park managers can use the finding that reducing management intensity and increasing vegetation diversity enhances multifunctionality, guiding cost-effective maintenance.

🏛政策担当者:Policymakers in rapidly urbanizing cities can apply the spatially explicit framework to prioritize vegetation heterogeneity in park planning and avoid over-management.

インフラ 都市公園はグリーンインフラの非常に重要な要素であり、微気候の調節、炭素隔離、生物多様性の保全、レクリエーションなどの多様な生態系サービスを提供する。しかし、面積自体が公園の性能を決定づけるわけではない。本論文は、景観コンテキスト、管理強度、植生の不均一性という相互に関連する3つの側面の組み合わせが、急速に都市化が進む亜熱帯メガシティである成都における都市公園の多機能性に集合的に影響を与える可能性を検討する。層別3×3×3サンプリングデザインを適用し、公園周辺の不浸透面被覆、維持管理強度、植生多様性の勾配に基づいて公園を分類し、19の機能組み合わせの下で60の代表的な公園を抽出した。多機能性は、冷却強度、炭素貯留、生物多様性、レクリエーション価値に基づいて測定された。空間的自己相関はMoran's Iを用いて分析し、グローバルおよびローカルの要因を測定するためにOrdinary Least Squares（OLS）と地理的加重回帰（GWR）を使用した。調査結果は、植生の不均一性が多機能性の最も有意な正の予測因子であり（β = 4.04, p < 0.001）、生物多様性と冷却能力に有意な正の影響を与えることを示している。しかし、管理強度は強い負の相関を示し（β = −0.058, p < 0.001）、集約的な維持管理レジームにおける生態学的トレードオフの理論を示唆している。内部構成を考慮した場合、公園面積と周辺の不浸透面被覆の独立した影響は内部構成よりも弱い。多機能性は空間的に高度にクラスタリングされており、これは主に空間分析を用いて特定され、GWRは局所的な変動を捕捉することでモデル性能（Adjusted R2 = 0.78）を大幅に向上させることが確認された。結果は、都市公園の多機能性は公園の規模ではなく、内部の生態学的複雑性と都市の圧力との間の相互作用の結果として発達することを示している。植生の多様性を向上させ、特に人口密集地域における緩和された空間的に差別化された管理アプローチは、生態系サービスの提供に大きく貢献できる。提案された層別かつ空間的に明示的なモデルは、急速に発展する都市地域における都市公園計画の最適化に向けた一般化可能なモデルを提供する。 参考文献 Ahmed, I., & Asif, M. (2026a). The Role of HR in Managing Quiet Quitting and Employee Disengagement in Gen Z Employees of Telecom Sector. Policy Journal of Social Science Review, 4(6), 118-151. https://doi.org/10.5281/zenodo.20581688 Ahmed, S., & Asif, M. (2026b). The impact of hybrid working on employee well-being with the moderating role of organizational performance: A case study of IT sector in Pakistan. Qualitative Research Journal for Social Studies, 3(2), 1006-1030. https://doi.org/10.63878/qrjs1173 Ashinze, U. K., Edeigba, B. A., Umoh, A. A., Biu, P. W., & Daraojimba, A. I. (2024). Urban green infrastructure and its role in sustainable cities: A comprehensive review. World Journal of Advanced Research and Reviews, 21(2), 928–936. Asif, M., Abid, M., & Riaz, A. (2026). Psychological drivers of investment decision making: A multi‑bias analysis of an emerging market’s retail investors. Contemporary Journal of Social Science Review, 4(2), 677–688. https://doi.org/10.63878/cjssr.v4i2.2608 Aznarez, C., Svenning, J.-C., Taveira, G., Baró, F., & Pascual, U. (2022). Wildness and habitat quality drive spatial patterns of urban biodiversity. Landscape and Urban Planning, 228, 104570. https://doi.org/10.1016/j.landurbplan.2022.104570 Borysiak, J., & Stępniewska, M. (2022). Perception of the vegetation cover pattern promoting biodiversity in urban parks by future greenery managers. Land, 11(3), 341. https://doi.org/10.3390/land11030341 Chen, L., Peng, P., Zhu, E., Wu, H., & Feng, D. (2025). Fairness of urban park layout from the perspective of multidimensional supply and demand relationship. Urban Forestry & Urban Greening, 102, 129016. https://doi.org/10.1016/j.ufug.2025.129016 Dizdaroglu, D. (2022). Developing design criteria for sustainable urban parks. Journal of Contemporary Urban Affairs, 6(1), 69–81. https://doi.org/10.25034/ijcua.2022.v6n1-6 Fan, L., Cui, X., & Wang, G. (2024). Impact of urban functional dynamics on surface temperature: A case study of Chengdu. Land, 13(12), 2181. https://doi.org/10.3390/land13122181 Giedych, R., Maksymiuk, G., & Cieszewska, A. (2024). Eco-spatial indices as an effective tool for climate change adaptation in residential neighbourhoods—Comparative study. Land, 13(9), 1492. https://doi.org/10.3390/land13091492 Guo, S., Yang, G., Pei, T., Ma, T., Song, C., Shu, H., Du, Y., & Zhou, C. (2019). Analysis of factors affecting urban park service area in Beijing: Perspectives from multi-source geographic data. Landscape and Urban Planning, 181, 103–117. https://doi.org/10.1016/j.landurbplan.2018.10.001 Haase, D., Larondelle, N., Andersson, E., Artmann, M., Borgström, S., Breuste, J., Gomez-Baggethun, E., Gren, Å., Hamstead, Z., & Hansen, R. (2014). A quantitative review of urban ecosystem service assessments: Concepts, models, and implementation. Ambio, 43(4), 413–433. https://doi.org/10.1007/s13280-014-0504-0 Halecki, W., Stachura, T., Fudała, W., Stec, A., & Kuboń, S. (2023). Assessment and planning of green spaces in urban parks: A review. Sustainable Cities and Society, 88, 104280. https://doi.org/10.1016/j.scs.2022.104280 Han, D., Zhang, T., Qin, Y., Tan, Y., & Liu, J. (2023). A comparative review on the mitigation strategies of urban heat island (UHI): A pathway for sustainable urban development. Climate and Development, 15(5), 379–403. https://doi.org/10.1080/17565529.2022.2081437 Iqbal, U. (2024). AI-enhanced network optimization for electric vehicle charging infrastructure expansion in the United States using graph theory and demand analytics. Journal of Engineering and Computational Intelligence Review, 2(2), 112–129. Iqbal, U. (2025a). AI-driven predictive maintenance for US smart manufacturing: Deep learning models for equipment failure prediction and operational resilience. Journal of Engineering and Computational Intelligence Review, 3(1), 114–138. Iqbal, U. (2025b). AI-powered supplier risk intelligence: Predicting financial and geopolitical supply chain disruptions in US critical industries. Journal of Engineering and Computational Intelligence Review, 3(2), 173–193. Iqbal, U., Bekmez, S., & Qurashi, F. A. (2026). Operational risk management through machine learning and business intelligence in US businesses. Spanish Journal of Innovation and Integrity, 54, 239–253. Iqbal, U., & Bhutto, Y. (2026). Digital transformation through artificial intelligence and advance business analytic in American operational management. Journal of Theoretical and Applied Econometrics, 3(1), 37–50. Jimenez, M. P., Elliott, E. G., DeVille, N. V., Laden, F., Hart, J. E., Weuve, J., Grodstein, F., & James, P. (2022). Residential green space and cognitive function in a large cohort of middle-aged women. JAMA Network Open, 5(4), e229306. https://doi.org/10.1001/jamanetworkopen.2022.9306 Khan, R. D. A., Ping, H., & Asif, M. (2026). The impact of green human resource management on employee green performance through green commitment and transformational leadership. Center for Management Science Research, 4(5), 635–677. https://doi.org/10.5281/zenodo.20510765 Kodym, A., Lapin, K., & Sanyal, D. (2025). Ecological connectivity in urban and semi-urban forests. In Ecological connectivity of forest ecosystems (pp. 365–381). Springer. https://doi.org/10.1007/978-3-031-89412-0_16 Li, X., Li, X., Zhang, M., Luo, Q., Li, Y., & Dong, L. (2024). Urban park attributes as predictors for the diversity and composition of spontaneous plants: A case in Beijing, China. Urban Forestry & Urban Greening, 91, 128185. https://doi.org/10.1016/j.ufug.2023.128185 Lin, B. B., Gaston, K. J., Fuller, R. A., Wu, D., Bush, R., & Shanahan, D. F. (2017). How green is your garden? Urban form and socio-demographic factors influence yard vegetation, visitation, and ecosystem service benefits. Landscape and Urban Planning, 157, 239–246. https://doi.org/10.1016/j.landurbplan.2016.07.007 Ma, Q., Zhang, J., & Li, Y. (2024). Advanced integration of urban street greenery and pedestrian flow: A multidimensional analysis in Chengdu's central urban district. ISPRS International Journal of Geo-Information, 13(7), 254. https://doi.org/10.3390/ijgi13070254 Meng, F., Ren, Z., Zhang, P., Wang, C., Hong, S., Geng, R., Hong, W., Wang, X., Huang, B., & Zhang, B. (2025). Estimation of the relationship between urban landscape pattern and crop yield by remote sensing data and field measurement. Remote Sensing, 17(22), 3667. https://doi.org/10.3390/rs17223667 Mexia, T., Vieira, J., Príncipe, A., Anjos, A., Silva, P., Lopes, N., Freitas, C., Santos-Reis, M., Correia, O., & Branquinho, C. (2018). Ecosystem services: Urban parks under a magnifying glass. Environmental Research, 160, 469–478. https://doi.org/10.1016/j.envres.2017.10.023 Miao, X., Pan, Y., Chen, H., Zhang, M.-J., Hu, W., Li, Y., Wu, R., Wang, P., Fang, S., & Niu, K. (2023). Understanding spontaneous biodiversity in informal urban green spaces: A local-landscape filtering framework with a test on wall plants. Urban Forestry & Urban Greening, 86, 127996. https://doi.org/10.1016/j.ufug.2023.127996 Priya, U. K., & Senthil, R. (2024). Framework for enhancing urban living through sustainable plant selection in residential green spaces. Urban Science, 8(4), 235. https://doi.org/10.3390/urbansci8040235 Szulczewska, B., Giedych, R., & Maksymiuk, G. (2017). Urban park as a subject of research in the 21st century in Poland, on the basis of CEON database. Architektura Krajobrazu, 4, 16–31. Tams, L., Paton, E. N., & Kluge, B. (2023). Impact of shading on evapotranspiration and water

AI 翻訳（deepseek-v4-flash）。 正確を期す場合は下の原文を参照してください。

Infrastructure Urban parks are very essential elements of green infrastructure, which provide various ecosystems services such as regulation of microclimates, sequestration of carbon, preservation of biodiversity, and recreation. But the size itself does not dictate the performance of parks. The paper examines the potential of the trio of linked aspects landscape context, management intensity, and vegetation heterogeneity to collectively influence urban park multifunctionality in Chengdu, a fast-urbanizing subtropical mega city. A stratified 3 x 3 x 3 sampling design was applied to categorize the parks by gradients of impervious cover surrounding maintenance intensity surrounding the park and vegetation diversity giving the 60 representative parks under 19 functional combinations. Multifunctionality has been measured based on the cooling intensity, storage of carbon, biodiversity, and recreational value. Spatial autocorrelations were studied with the help of the Morans I, and Ordinary Least Squares (OLS) and geographically weighted regression (GWR) were used to measure both global and local drivers. Findings show that vegetation heterogeneity is the most significant positive predictor of multifunctionality (β = 4.04, p < 0.001), which has a significant positive impact on biodiversity and cooling capacity. However, the management intensity exhibits a strong negative correlation (β = −0.058, p < 0.001), which indicates the theory of making ecological trade-offs in the intensive maintenance regimes. Independent effects of park area and surrounding impervious cover are weaker than internal composition when internal composition is taken into consideration. Multifunctionality is highly clustered in space, which is mainly identified using spatial analysis, and GWR is seen to enhance the model performance (Adjusted R2 = 0.78) considerably by capturing the local variation. The results have shown that, urban park multifunctionality develops as a result of interaction between inner ecological complexity and urban pressures instead of park size. Improving the vegetation diversity and moderated and spatially differentiated management approaches especially in densely populated areas can significantly aid in delivering ecosystem services. The suggested stratified and spatially explicit model offers a generalizable model towards the optimization of urban park planning in fast developing urban areas. References Ahmed, I., & Asif, M. (2026a). The Role of HR in Managing Quiet Quitting and Employee Disengagement in Gen Z Employees of Telecom Sector. Policy Journal of Social Science Review, 4(6), 118-151. https://doi.org/10.5281/zenodo.20581688 Ahmed, S., & Asif, M. (2026b). The impact of hybrid working on employee well-being with the moderating role of organizational performance: A case study of IT sector in Pakistan. Qualitative Research Journal for Social Studies, 3(2), 1006-1030. https://doi.org/10.63878/qrjs1173 Ashinze, U. K., Edeigba, B. A., Umoh, A. A., Biu, P. W., & Daraojimba, A. I. (2024). Urban green infrastructure and its role in sustainable cities: A comprehensive review. World Journal of Advanced Research and Reviews, 21(2), 928–936. Asif, M., Abid, M., & Riaz, A. (2026). Psychological drivers of investment decision making: A multi‑bias analysis of an emerging market’s retail investors. Contemporary Journal of Social Science Review, 4(2), 677–688. https://doi.org/10.63878/cjssr.v4i2.2608 Aznarez, C., Svenning, J.-C., Taveira, G., Baró, F., & Pascual, U. (2022). Wildness and habitat quality drive spatial patterns of urban biodiversity. Landscape and Urban Planning, 228, 104570. https://doi.org/10.1016/j.landurbplan.2022.104570 Borysiak, J., & Stępniewska, M. (2022). Perception of the vegetation cover pattern promoting biodiversity in urban parks by future greenery managers. Land, 11(3), 341. https://doi.org/10.3390/land11030341 Chen, L., Peng, P., Zhu, E., Wu, H., & Feng, D. (2025). Fairness of urban park layout from the perspective of multidimensional supply and demand relationship. Urban Forestry & Urban Greening, 102, 129016. https://doi.org/10.1016/j.ufug.2025.129016 Dizdaroglu, D. (2022). Developing design criteria for sustainable urban parks. Journal of Contemporary Urban Affairs, 6(1), 69–81. https://doi.org/10.25034/ijcua.2022.v6n1-6 Fan, L., Cui, X., & Wang, G. (2024). Impact of urban functional dynamics on surface temperature: A case study of Chengdu. Land, 13(12), 2181. https://doi.org/10.3390/land13122181 Giedych, R., Maksymiuk, G., & Cieszewska, A. (2024). Eco-spatial indices as an effective tool for climate change adaptation in residential neighbourhoods—Comparative study. Land, 13(9), 1492. https://doi.org/10.3390/land13091492 Guo, S., Yang, G., Pei, T., Ma, T., Song, C., Shu, H., Du, Y., & Zhou, C. (2019). Analysis of factors affecting urban park service area in Beijing: Perspectives from multi-source geographic data. Landscape and Urban Planning, 181, 103–117. https://doi.org/10.1016/j.landurbplan.2018.10.001 Haase, D., Larondelle, N., Andersson, E., Artmann, M., Borgström, S., Breuste, J., Gomez-Baggethun, E., Gren, Å., Hamstead, Z., & Hansen, R. (2014). A quantitative review of urban ecosystem service assessments: Concepts, models, and implementation. Ambio, 43(4), 413–433. https://doi.org/10.1007/s13280-014-0504-0 Halecki, W., Stachura, T., Fudała, W., Stec, A., & Kuboń, S. (2023). Assessment and planning of green spaces in urban parks: A review. Sustainable Cities and Society, 88, 104280. https://doi.org/10.1016/j.scs.2022.104280 Han, D., Zhang, T., Qin, Y., Tan, Y., & Liu, J. (2023). A comparative review on the mitigation strategies of urban heat island (UHI): A pathway for sustainable urban development. Climate and Development, 15(5), 379–403. https://doi.org/10.1080/17565529.2022.2081437 Iqbal, U. (2024). AI-enhanced network optimization for electric vehicle charging infrastructure expansion in the United States using graph theory and demand analytics. Journal of Engineering and Computational Intelligence Review, 2(2), 112–129. Iqbal, U. (2025a). AI-driven predictive maintenance for US smart manufacturing: Deep learning models for equipment failure prediction and operational resilience. Journal of Engineering and Computational Intelligence Review, 3(1), 114–138. Iqbal, U. (2025b). AI-powered supplier risk intelligence: Predicting financial and geopolitical supply chain disruptions in US critical industries. Journal of Engineering and Computational Intelligence Review, 3(2), 173–193. Iqbal, U., Bekmez, S., & Qurashi, F. A. (2026). Operational risk management through machine learning and business intelligence in US businesses. Spanish Journal of Innovation and Integrity, 54, 239–253. Iqbal, U., & Bhutto, Y. (2026). Digital transformation through artificial intelligence and advance business analytic in American operational management. Journal of Theoretical and Applied Econometrics, 3(1), 37–50. Jimenez, M. P., Elliott, E. G., DeVille, N. V., Laden, F., Hart, J. E., Weuve, J., Grodstein, F., & James, P. (2022). Residential green space and cognitive function in a large cohort of middle-aged women. JAMA Network Open, 5(4), e229306. https://doi.org/10.1001/jamanetworkopen.2022.9306 Khan, R. D. A., Ping, H., & Asif, M. (2026). The impact of green human resource management on employee green performance through green commitment and transformational leadership. Center for Management Science Research, 4(5), 635–677. https://doi.org/10.5281/zenodo.20510765 Kodym, A., Lapin, K., & Sanyal, D. (2025). Ecological connectivity in urban and semi-urban forests. In Ecological connectivity of forest ecosystems (pp. 365–381). Springer. https://doi.org/10.1007/978-3-031-89412-0_16 Li, X., Li, X., Zhang, M., Luo, Q., Li, Y., & Dong, L. (2024). Urban park attributes as predictors for the diversity and composition of spontaneous plants: A case in Beijing, China. Urban Forestry & Urban Greening, 91, 128185. https://doi.org/10.1016/j.ufug.2023.128185 Lin, B. B., Gaston, K. J., Fuller, R. A., Wu, D., Bush, R., & Shanahan, D. F. (2017). How green is your garden? Urban form and socio-demographic factors influence yard vegetation, visitation, and ecosystem service benefits. Landscape and Urban Planning, 157, 239–246. https://doi.org/10.1016/j.landurbplan.2016.07.007 Ma, Q., Zhang, J., & Li, Y. (2024). Advanced integration of urban street greenery and pedestrian flow: A multidimensional analysis in Chengdu's central urban district. ISPRS International Journal of Geo-Information, 13(7), 254. https://doi.org/10.3390/ijgi13070254 Meng, F., Ren, Z., Zhang, P., Wang, C., Hong, S., Geng, R., Hong, W., Wang, X., Huang, B., & Zhang, B. (2025). Estimation of the relationship between urban landscape pattern and crop yield by remote sensing data and field measurement. Remote Sensing, 17(22), 3667. https://doi.org/10.3390/rs17223667 Mexia, T., Vieira, J., Príncipe, A., Anjos, A., Silva, P., Lopes, N., Freitas, C., Santos-Reis, M., Correia, O., & Branquinho, C. (2018). Ecosystem services: Urban parks under a magnifying glass. Environmental Research, 160, 469–478. https://doi.org/10.1016/j.envres.2017.10.023 Miao, X., Pan, Y., Chen, H., Zhang, M.-J., Hu, W., Li, Y., Wu, R., Wang, P., Fang, S., & Niu, K. (2023). Understanding spontaneous biodiversity in informal urban green spaces: A local-landscape filtering framework with a test on wall plants. Urban Forestry & Urban Greening, 86, 127996. https://doi.org/10.1016/j.ufug.2023.127996 Priya, U. K., & Senthil, R. (2024). Framework for enhancing urban living through sustainable plant selection in residential green spaces. Urban Science, 8(4), 235. https://doi.org/10.3390/urbansci8040235 Szulczewska, B., Giedych, R., & Maksymiuk, G. (2017). Urban park as a subject of research in the 21st century in Poland, on the basis of CEON database. Architektura Krajobrazu, 4, 16–31. Tams, L., Paton, E. N., & Kluge, B. (2023). Impact of shading on evapotranspiration and water

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